Audio coding is the domain of signal compression that deals with exploiting redundancy and irrelevancy in audio signals using psychoacoustic knowledge. Today audio codecs typically need around 60 kbps/channel for perceptually transparent coding of almost any type of audio signal. Newer codecs are aimed at reducing the coding bitrate by exploiting spectral similarities in the signal using techniques such as bandwidth extension (BWE). A BWE scheme uses a low bitrate parameter set to represent the high frequency (HF) components of an audio signal. The HF spectrum is filled up with spectral content from low frequency (LF) regions and the spectral shape, tilt and temporal continuity adjusted to maintain the timbre and color of the original signal. Such BWE methods enable audio codecs to retain good quality at even low bitrates of around 24 kbps/channel.
The inventive audio coding system efficiently codes arbitrary audio signals at a wide range of bitrates. Whereas, for high bitrates, the inventive system converges to transparency, for low bitrates perceptual annoyance is minimized. Therefore, the main share of available bitrate is used to waveform code just the perceptually most relevant structure of the signal in the encoder, and the resulting spectral gaps are filled in the decoder with signal content that roughly approximates the original spectrum. A very limited bit budget is consumed to control the parameter driven so-called spectral Intelligent Gap Filling (IGF) by dedicated side information transmitted from the encoder to the decoder.
Storage or transmission of audio signals is often subject to strict bitrate constraints. In the past, coders were forced to drastically reduce the transmitted audio bandwidth when only a very low bitrate was available.
Modern audio codecs are nowadays able to code wide-band signals by using bandwidth extension (BWE) methods [1]. These algorithms rely on a parametric representation of the high-frequency content (HF)—which is generated from the waveform coded low-frequency part (LF) of the decoded signal by means of transposition into the HF spectral region (“patching”) and application of a parameter driven post processing. In BWE schemes, the reconstruction of the HF spectral region above a given so-called cross-over frequency is often based on spectral patching. Typically, the HF region is composed of multiple adjacent patches and each of these patches is sourced from band-pass (BP) regions of the LF spectrum below the given cross-over frequency. State-of-the-art systems efficiently perform the patching within a filterbank representation, e.g. Quadrature Mirror Filterbank (QMF), by copying a set of adjacent subband coefficients from a source to the target region.
Another technique found in today's audio codecs that increases compression efficiency and thereby enables extended audio bandwidth at low bitrates is the parameter driven synthetic replacement of suitable parts of the audio spectra. For example, noise-like signal portions of the original audio signal can be replaced without substantial loss of subjective quality by artificial noise generated in the decoder and scaled by side information parameters. One example is the Perceptual Noise Substitution (PNS) tool contained in MPEG-4 Advanced Audio Coding (AAC) [5].
A further provision that also enables extended audio bandwidth at low bitrates is the noise filling technique contained in MPEG-D Unified Speech and Audio Coding (USAC) [7]. Spectral gaps (zeroes) that are inferred by the dead-zone of the quantizer due to a too coarse quantization, are subsequently filled with artificial noise in the decoder and scaled by a parameter-driven post-processing.
Another state-of-the-art system is termed Accurate Spectral Replacement (ASR) [2-4]. In addition to a waveform codec, ASR employs a dedicated signal synthesis stage which restores perceptually important sinusoidal portions of the signal at the decoder. Also, a system described in [5] relies on sinusoidal modeling in the HF region of a waveform coder to enable extended audio bandwidth having decent perceptual quality at low bitrates. All these methods involve transformation of the data into a second domain apart from the Modified Discrete Cosine Transform (MDCT) and also fairly complex analysis/synthesis stages for the preservation of HF sinusoidal components.
FIG. 13a illustrates a schematic diagram of an audio encoder for a bandwidth extension technology as, for example, used in High Efficiency Advanced Audio Coding (HE-AAC). An audio signal at line 1300 is input into a filter system comprising of a low pass 1302 and a high pass 1304. The signal output by the high pass filter 1304 is input into a parameter extractor/coder 1306. The parameter extractor/coder 1306 is configured for calculating and coding parameters such as a spectral envelope parameter, a noise addition parameter, a missing harmonics parameter, or an inverse filtering parameter, for example. These extracted parameters are input into a bit stream multiplexer 1308. The low pass output signal is input into a processor typically comprising the functionality of a down sampler 1310 and a core coder 1312. The low pass 1302 restricts the bandwidth to be encoded to a significantly smaller bandwidth than occurring in the original input audio signal on line 1300. This provides a significant coding gain due to the fact that the whole functionalities occurring in the core coder only have to operate on a signal with a reduced bandwidth. When, for example, the bandwidth of the audio signal on line 1300 is 20 kHz and when the low pass filter 1302 exemplarily has a bandwidth of 4 kHz, in order to fulfill the sampling theorem, it is theoretically sufficient that the signal subsequent to the down sampler has a sampling frequency of 8 kHz, which is a substantial reduction to the sampling rate that may be used for the audio signal 1300 which has to be at least 40 kHz.
FIG. 13b illustrates a schematic diagram of a corresponding bandwidth extension decoder. The decoder comprises a bitstream multiplexer 1320. The bitstream demultiplexer 1320 extracts an input signal for a core decoder 1322 and an input signal for a parameter decoder 1324. A core decoder output signal has, in the above example, a sampling rate of 8 kHz and, therefore, a bandwidth of 4 kHz while, for a complete bandwidth reconstruction, the output signal of a high frequency reconstructor 1330 is at 20 kHz requiring a sampling rate of at least 40 kHz. In order to make this possible, a decoder processor having the functionality of an upsampler 1325 and a filterbank 1326 may be used. The high frequency reconstructor 1330 then receives the frequency-analyzed low frequency signal output by the filterbank 1326 and reconstructs the frequency range defined by the high pass filter 1304 of FIG. 13a using the parametric representation of the high frequency band. The high frequency reconstructor 1330 has several functionalities such as the regeneration of the upper frequency range using the source range in the low frequency range, a spectral envelope adjustment, a noise addition functionality and a functionality to introduce missing harmonics in the upper frequency range and, if applied and calculated in the encoder of FIG. 13a, an inverse filtering operation in order to account for the fact that the higher frequency range is typically not as tonal as the lower frequency range. In HE-AAC, missing harmonics are re-synthesized on the decoder-side and are placed exactly in the middle of a reconstruction band. Hence, all missing harmonic lines that have been determined in a certain reconstruction band are not placed at the frequency values where they were located in the original signal. Instead, those missing harmonic lines are placed at frequencies in the center of the certain band. Thus, when a missing harmonic line in the original signal was placed very close to the reconstruction band border in the original signal, the error in frequency introduced by placing this missing harmonics line in the reconstructed signal at the center of the band is close to 50% of the individual reconstruction band, for which parameters have been generated and transmitted.
Furthermore, even though the typical audio core coders operate in the spectral domain, the core decoder nevertheless generates a time domain signal which is then, again, converted into a spectral domain by the filter bank 1326 functionality. This introduces additional processing delays, may introduce artifacts due to tandem processing of firstly transforming from the spectral domain into the frequency domain and again transforming into typically a different frequency domain and, of course, this also involves a substantial amount of computation complexity and thereby electric power, which is specifically an issue when the bandwidth extension technology is applied in mobile devices such as mobile phones, tablet or laptop computers, etc.
Current audio codecs perform low bitrate audio coding using BWE as an integral part of the coding scheme. However, BWE techniques are restricted to replace high frequency (HF) content only. Furthermore, they do not allow perceptually important content above a given cross-over frequency to be waveform coded. Therefore, contemporary audio codecs either lose HF detail or timbre when the BWE is implemented, since the exact alignment of the tonal harmonics of the signal is not taken into consideration in most of the systems.
Another shortcoming of the current state of the art BWE systems is the need for transformation of the audio signal into a new domain for implementation of the BWE (e.g. transform from MDCT to QMF domain). This leads to complications of synchronization, additional computational complexity and increased memory requirements.
Storage or transmission of audio signals is often subject to strict bitrate constraints. In the past, coders were forced to drastically reduce the transmitted audio bandwidth when only a very low bitrate was available. Modern audio codecs are nowadays able to code wide-band signals by using bandwidth extension (BWE) methods [1-2]. These algorithms rely on a parametric representation of the high-frequency content (HF)—which is generated from the waveform coded low-frequency part (LF) of the decoded signal by means of transposition into the HF spectral region (“patching”) and application of a parameter driven post processing.
In BWE schemes, the reconstruction of the HF spectral region above a given so-called cross-over frequency is often based on spectral patching. Other schemes that are functional to fill spectral gaps, e.g. Intelligent Gap Filling (IGF), use neighboring so-called spectral tiles to regenerate parts of audio signal HF spectra. Typically, the HF region is composed of multiple adjacent patches or tiles and each of these patches or tiles is sourced from band-pass (BP) regions of the LF spectrum below the given cross-over frequency. State-of-the-art systems efficiently perform the patching or tiling within a filterbank representation by copying a set of adjacent subband coefficients from a source to the target region. Yet, for some signal content, the assemblage of the reconstructed signal from the LF band and adjacent patches within the HF band can lead to beating, dissonance and auditory roughness.
Therefore, in [19], the concept of dissonance guard-band filtering is presented in the context of a filterbank-based BWE system. It is suggested to effectively apply a notch filter of approx. 1 Bark bandwidth at the cross-over frequency between LF and BWE-regenerated HF to avoid the possibility of dissonance and replace the spectral content with zeros or noise.
However, the proposed solution in [19] has some drawbacks: First, the strict replacement of spectral content by either zeros or noise can also impair the perceptual quality of the signal. Moreover, the proposed processing is not signal adaptive and can therefore harm perceptual quality in some cases. For example, if the signal contains transients, this can lead to pre- and post-echoes.
Second, dissonances can also occur at transitions between consecutive HF patches. The proposed solution in [19] is only functional to remedy dissonances that occur at cross-over frequency between LF and BWE-regenerated HF.
Last, as opposed to filter bank based systems like proposed in [19], BWE systems can also be realized in transform based implementations, like e.g. the Modified Discrete Cosine Transform (MDCT). Transforms like MDCT are very prone to so-called warbling [20] or ringing artifacts that occur if bandpass regions of spectral coefficients are copied or spectral coefficients are set to zero like proposed in [19].
Particularly, U.S. Pat. No. 8,412,365 discloses to use, in filterbank based translation or folding, so-called guard-bands which are inserted and made of one or several subband channels set to zero. A number of filterbank channels is used as guard-bands, and a bandwidth of a guard-band should be 0.5 Bark. These dissonance guard-bands are partially reconstructed using random white noise signals, i.e., the subbands are fed with white noise instead of being zero. The guard bands are inserted irrespective of the current signal to processed.
Bandwidth extension systems are particularly problematic when they are realized in transform-based implementations like, for example, the Modified Discrete Cosine Transform (MDCT).
Transforms like MDCT and other transforms as well are very prone to so-called warbling as discussed in [3] and ringing artifacts that occur if bandpass regions of spectral coefficients are copied or spectral coefficients are set to zero like proposed in [2].